Naamah Bloch

Vasculogenic mimicry (VM) is a non-endothelial mode of tumor vascularization in which aggressive cancer cells form vessel-like networks that support microcirculation, metastasis, and resistance to anti-angiogenic therapies. VM is particularly prominent in triple-negative breast cancer (TNBC), but its molecular regulators remain incompletely understood. Focal adhesion kinase (FAK) and its paralog, proline-rich tyrosine kinase 2 (Pyk2), are closely related non-receptor tyrosine kinases implicated in epithelial-to-mesenchymal transition (EMT), invasion, and metastasis in TNBC. However, their roles in VM have not been defined. Here we perform transcriptomic analysis of FAK and Pyk2 clinical expression patterns using TNMplot V2, DepMap, and patient cohort datasets to systematically dissect the distinct contributions of FAK and Pyk2 to VM in TNBC. Our in vitro tube formation assay shows that in TNBC cells, knockdown of FAK, but not Pyk2, results in failure to form robust 3D vessel-like networks in Matrigel. Similarly, overexpression of Pyk2, but not FAK, in TNBC cells results in poor vessel-like network formation. Consistent with these findings, analysis of two independent patient cohorts (TCGA-BRCA and METABRIC) revealed selective upregulation of FAK in TNBC, while Pyk2 was inversely associated with vasculogenic-mimicry-associated gene expression, supporting the opposing roles of the two kinases in patient tumors. Taken together, these findings establish that FAK and Pyk2 govern VM through non-redundant, kinase-specific, and functionally opposed mechanisms: FAK acting as a positive regulator of VM, and Pyk2 as a context dependent suppressor of VM at elevated levels. These results nominate FAK as a candidate target for suppressing VM-driven tumor perfusion in TNBC and suggest that dual FAK/Pyk2 inhibition warrants caution hypotheses that remain to be tested pharmacologically.

Background: Vasculogenic mimicry (VM) is a tumor-driven vascularization strategy in which aggressive cancer cells form perfusable, endothelium-independent channels that support tumor growth, metastasis, and therapy resistance. VM is prevalent in triple-negative breast cancer (TNBC), but within this group of tumors, VM heterogeneity is underexplored. Likewise, VM competence and its relationship to classical endothelial angiogenesis (EA) remain incompletely understood. Methods: Here, as a proof of concept, we combine functional analysis of three molecularly distinct TNBC cell lines with a panel-wide DepMap transcriptomic survey to characterize VM heterogeneity. Results: Using an in vitro tube formation assay, we show that the VM-competent TNBC cell lines MDA-MB-231 and MDA-MB-231-4175 form robust 3D vessel-like networks in a matrigel matrix, whereas the VM-incompetent line MDA-MB-468 does not. As a control, we use an immortalized endothelial cell line, 3B-11, that forms classical EA vessel-like networks. Moreover, we visualize VM (Laminin-5⁺) and EA (CD31⁺) markers in vessel-like networks of VM-competent TNBC xenografts using immunohistochemical staining and show that while they are distinctly labeled, they can also coexist to form mosaic-like vessels. Then, we use DepMap-based transcription profiles and reveal that VM competence is associated with a distinct signature. Interestingly, VM and EA transcription profiles partially overlap, yet they also remain transcriptionally distinct, with inferred mechanistic divergence, with VM being more associated with cancer cell stemness (CSC), epithelial-to-mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling programs and EA being more associated with vessel strength. In addition, VM-competent TNBC cells display migration patterns and transcriptomic features consistent with endothelial-like mechanosensitivity. Conclusions: Together, these findings indicate that VM is a distinct, heterogeneous, and therapy-relevant state in TNBC that complements classical angiogenesis. Finally, the mechanistic distinction between VM and EA programs made here will motivate future studies on dual-targeting strategies that inhibit both vascularization processes while also motivating future studies on VM for precision treatment in TNBC.

Parkinson’s disease (PD) has been associated with some types of food and drugs. Here, we query PubMed for the association of PD with foods and drugs, using a list of 217,776 compounds derived from the Human Metabolome Database (HMDB). To calculate associations, a Python script was developed to query PubMed for co-citations of PD with each compound, and adjust this count for compound abundance. Notably, PD is found to be associated with small-molecule drugs, adjunctive therapies, contraindicated drugs, diagnostic agents, biomarkers, conditional essential molecules, and inducers. Drugs include L-dopa (49%), carbidopa (63%), benserazide (50%), entacapone (74%), tolcapone (56%), rasagiline (76%), selegiline (46%), pargyline (4%), ropinirole (61%), pramipexole (56%), lisuride (27%), cabergoline (16%), bromocriptine (12%), and zonisamide (9%). Adjunctive therapies include droxidopa (33%), trihexyphenidyl (28%), biperiden (17%), amantadine (24%), memantine (7%), rivastigmine (13%), donepezil (6%), galantamine (4%), domperidone (6%), clonazepam (4%), tetrabenazine (16%), mazindol (13%), quetiapine (6%), and clozapine (4%). Contraindicated drugs include haloperidol (4%), sulpiride (3%), and methyldopa (6%). Diagnostic agents include FP-CIT (60%) and beta-CIT (43%). Biomarkers include 3-methoxytyrosine (48%) and homovanillic acid (12%). Endogenous cofactors include tetrahydrobiopterin (4%) and Coenzyme Q10 (4%). Chemical inducers of PD include 6-hydroxydopamine (40%), N-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 78%), tetrahydropyridine (77%), probenecid (4%), quinolinic acid (4%), 1,2,3,4-tetrahydroisoquinoline (TIQ, 16%), salsolinol (32%), rotenone (25%), and β-Methylamino-L-alanine (BMAA, 29%). Notably, our study highlights conditional essential endogenous cofactors associated with PD and emphasizes rational directions for investigation in PD.

Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer’s disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.

Malaria is a prevalent parasitic disease that is estimated to kill between one and two million people—mostly children—every year. Here, we query PubMed for malaria drug resistance and plot the yearly citations of 14 common antimalarials. Remarkably, most antimalarial drugs display cyclic resistance patterns, rising and falling over four decades. The antimalarial drugs that exhibit cyclic resistance are quinine, chloroquine, mefloquine, amodiaquine, artesunate, artemether, sulfadoxine, doxycycline, halofantrine, piperaquine, pyrimethamine, atovaquone, artemisinin, and dihydroartemisinin. Exceptionally, the resistance of the two latter drugs can also correlate with a linear rise. Our predicted antimalarial drug resistance is consistent with clinical data reported by the Worldwide Antimalarial Resistance Network (WWARN) and validates our methodology. Notably, the cyclical resistance suggests that most antimalarial drugs are sustainable in the end. Furthermore, cyclic resistance is clinically relevant and discourages routine monotherapy, in particular, while resistance is on the rise. Finally, cyclic resistance encourages the combination of antimalarial drugs at distinct phases of resistance.

Alzheimer’s disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.

Adult neurogenesis is a complex physiological process, which plays a central role in maintaining cognitive functions, and consists of progenitor cell proliferation, newborn cell migration, and cell maturation. Adult neurogenesis is susceptible to alterations under various physiological and pathological conditions. A substantial decay of neurogenesis has been documented in Alzheimer’s disease (AD) patients and animal AD models; however, several treatment strategies can halt any further decline and even induce neurogenesis. Our previous results indicated a potential effect of arginase inhibition, with norvaline, on various aspects of neurogenesis in triple-transgenic mice. To better evaluate this effect, we chronically administered an arginase inhibitor, norvaline, to triple-transgenic and wild-type mice, and applied an advanced immunohistochemistry approach with several biomarkers and bright-field microscopy. Remarkably, we evidenced a significant reduction in the density of neuronal progenitors, which demonstrate a different phenotype in the hippocampi of triple-transgenic mice as compared to wild-type animals. However, norvaline showed no significant effect upon the progenitor cell number and constitution. We demonstrated that norvaline treatment leads to an escalation of the polysialylated neuronal cell adhesion molecule immunopositivity, which suggests an improvement in the newborn neuron survival rate. Additionally, we identified a significant increase in the hippocampal microtubule-associated protein 2 stain intensity. We also explore the molecular mechanisms underlying the effects of norvaline on adult mice neurogenesis and provide insights into their machinery.